{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| paddlepaddle | python|\n",
    "| -------- | -------- | \n",
    "| 2.4.0     | 3.7.4     | "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1、环境配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:56:50.797255Z",
     "iopub.status.busy": "2025-02-23T07:56:50.796911Z",
     "iopub.status.idle": "2025-02-23T07:57:12.420648Z",
     "shell.execute_reply": "2025-02-23T07:57:12.419904Z",
     "shell.execute_reply.started": "2025-02-23T07:56:50.797235Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正克隆到 'PaddleSeg'...\r\n",
      "remote: Enumerating objects: 27363, done.\u001B[K\r\n",
      "remote: Counting objects: 100% (27363/27363), done.\u001B[K\r\n",
      "remote: Compressing objects: 100% (8752/8752), done.\u001B[K\r\n",
      "remote: Total 27363 (delta 18413), reused 27132 (delta 18182), pack-reused 0\u001B[K\r\n",
      "接收对象中: 100% (27363/27363), 349.10 MiB | 19.38 MiB/s, 完成.\r\n",
      "处理 delta 中: 100% (18413/18413), 完成.\r\n",
      "检查连接... 完成。\r\n"
     ]
    }
   ],
   "source": [
    "# 克隆PaddleSeg仓库, 执行一次就行\n",
    "!git clone https://gitee.com/paddlepaddle/PaddleSeg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:57:12.422283Z",
     "iopub.status.busy": "2025-02-23T07:57:12.421974Z",
     "iopub.status.idle": "2025-02-23T07:57:16.381328Z",
     "shell.execute_reply": "2025-02-23T07:57:16.380333Z",
     "shell.execute_reply.started": "2025-02-23T07:57:12.422263Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 创建文件夹 执行一次就行\n",
    "! mkdir AJDataset\n",
    "# 解压数据集 执行一次就行\n",
    "! unzip -oq /home/aistudio/data/data253280/安吉遥感语义分割数据集.zip -d AJDataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、数据集可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.1 可视化配置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:57:16.382732Z",
     "iopub.status.busy": "2025-02-23T07:57:16.382373Z",
     "iopub.status.idle": "2025-02-23T07:57:17.078403Z",
     "shell.execute_reply": "2025-02-23T07:57:17.077494Z",
     "shell.execute_reply.started": "2025-02-23T07:57:16.382712Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 导库\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 类别标签的颜色配置\n",
    "color_map = {\n",
    "    0: [0, 0, 0],        # '未知': 类别 0\n",
    "    1: [215, 200, 185],  # '裸地': 类别 1\n",
    "    2: [131, 194, 56],   # '草地': 类别 2\n",
    "    3: [241, 165, 180],  # '构筑': 类别 3\n",
    "    4: [210, 216, 201],  # '道路': 类别 4\n",
    "    5: [49, 173, 105],   # '林地': 类别 5\n",
    "    6: [163, 214, 245],  # '水域': 类别 6\n",
    "    7: [248, 208, 114],  # '耕地': 类别 7\n",
    "    8: [229, 103, 102]   # '房屋': 类别 8\n",
    "}\n",
    "\n",
    "\n",
    "def label2color(lable):\n",
    "    '''\n",
    "    将单通道灰度label, 根据颜色配置转为三通道彩色\n",
    "    '''\n",
    "    # 将图像转换为 numpy 数组\n",
    "    label_array = np.array(lable)\n",
    "    # 创建彩色图像\n",
    "    color_label_array = np.zeros((label_array.shape[0], label_array.shape[1], 3), dtype=np.uint8)\n",
    "\n",
    "    # 将单通道标签映射为彩色图像\n",
    "    for label_value, color in color_map.items():\n",
    "        color_label_array[label_array == label_value] = color\n",
    "    return color_label_array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.2 读取文件并可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:57:17.080595Z",
     "iopub.status.busy": "2025-02-23T07:57:17.079986Z",
     "iopub.status.idle": "2025-02-23T07:57:17.581615Z",
     "shell.execute_reply": "2025-02-23T07:57:17.580815Z",
     "shell.execute_reply.started": "2025-02-23T07:57:17.080564Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image path: AJDataset/images/AJ1_100.jpg, image size: (512, 512)\r\n",
      "label path: AJDataset/labels/AJ1_100.png, label size: (512, 512)\r\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 读取图像\n",
    "image_path = '../../data/images/AJ1_100.jpg'\n",
    "label_path = '../../data/labels/AJ1_100.png'\n",
    "\n",
    "image = Image.open(image_path)\n",
    "lable = Image.open(label_path)\n",
    "print(f\"image path: {image_path}, image size: {image.size}\")\n",
    "print(f\"label path: {label_path}, label size: {lable.size}\")\n",
    "\n",
    "# 可视化\n",
    "fig, axes = plt.subplots(1, 2, figsize=(1 * 5, 1 * 5))\n",
    "fig.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0.01, wspace=0.01)\n",
    "axes[0].imshow(image)\n",
    "axes[0].axis(\"off\")\n",
    "axes[0].set_title(\"image\")\n",
    "axes[1].imshow(label2color(lable))\n",
    "axes[1].axis(\"off\")\n",
    "axes[1].set_title(\"to color label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意数据集格式，详情可参考[自定义数据集](https://gitee.com/paddlepaddle/PaddleSeg/blob/release/2.8/docs/data/marker/marker_cn.md)。<br>\n",
    "本文挂载的数据集格式如下，详情可见[遥感影像【语义分割】](https://aistudio.baidu.com/datasetdetail/253280)。\n",
    "```\n",
    "AJDataset\n",
    "|─labels\n",
    "|   ├─AJ1_1.png\n",
    "|   ├─AJ1_2.png\n",
    "|─images\n",
    "|   ├─AJ1_1.jpg\n",
    "|   ├─AJ1_2.jpg\n",
    "|─train.txt\n",
    "|─val.txt\n",
    "|─test.txt\n",
    "\n",
    "......\n",
    "train.txt如下：\n",
    "images/AJ2_1456.jpg labels/AJ2_1456.png\n",
    "images/AJ2_2188.jpg labels/AJ2_2188.png\n",
    "\n",
    "val.txt如下：\n",
    "images/AJ2_3046.jpg labels/AJ2_3046.png\n",
    "images/AJ2_1753.jpg labels/AJ2_1753.png\n",
    "\n",
    "test.txt如下：\n",
    "images/AJ2_2173.jpg labels/AJ2_2173.png\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、模型构建与训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:57:17.584019Z",
     "iopub.status.busy": "2025-02-23T07:57:17.583618Z",
     "iopub.status.idle": "2025-02-23T07:57:17.621808Z",
     "shell.execute_reply": "2025-02-23T07:57:17.620938Z",
     "shell.execute_reply.started": "2025-02-23T07:57:17.583991Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 导入需要用到的库\n",
    "import sys\n",
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:57:17.623218Z",
     "iopub.status.busy": "2025-02-23T07:57:17.622839Z",
     "iopub.status.idle": "2025-02-23T07:57:17.628145Z",
     "shell.execute_reply": "2025-02-23T07:57:17.627384Z",
     "shell.execute_reply.started": "2025-02-23T07:57:17.623195Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 定义全局变量\n",
    "\n",
    "# 数据集存放目录\n",
    "DATA_ROOT = '/home/aistudio/data/'\n",
    "# 训练集file_list文件路径\n",
    "TRAIN_PATH='/home/aistudio/data/train.txt'\n",
    "# 验证集file_list文件路径\n",
    "VAL_PATH='/home/aistudio/data/val.txt'\n",
    "# 测试集file_list文件路径\n",
    "TEST_PATH='/home/aistudio/data/test.txt'\n",
    "# 模型训练和visualdl日志文件的保存根路径\n",
    "EXP_DIR = '/home/aistudio/PaddleSeg/output'\n",
    "# 测试集预测结果保存根路径\n",
    "TEST_SAVE_DIR = '/home/aistudio/PaddleSeg/output/results'\n",
    "# 训练迭代次数（1 iter = 1 batch , 即进行一次前向传播和反向梯度下降）\n",
    "# total iters = (total sample / batch size) * EPOCHS。若想使用EPOCHS，则需要知道total sample数量\n",
    "ITERS = 2000 # 这里只训练了不到2 epoch。样本数量6000左右，训练集5315，5315 // 4 = 1328（iter）。即1epoch可迭代1328次。\n",
    "# EPOCHS = 100 # 暂时没使用到\n",
    "# 单卡batch size\n",
    "BACH_SIZE = 4\n",
    "# 类别数(背景也算，即classes + background)\n",
    "NUM_CLASSES = 9\n",
    "# 图像大小\n",
    "SIZE = (512, 512)\n",
    "# 模型保存的间隔iter步数\n",
    "SAVE_ITERS = 10\n",
    "# 打印日志的间隔iter步数\n",
    "LOG_ITERS = 1\n",
    "# 用于异步读取数据的进程数量，大于等于1时开启子进程读取数据\n",
    "NUM_WORKERS = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:57:17.629664Z",
     "iopub.status.busy": "2025-02-23T07:57:17.629254Z",
     "iopub.status.idle": "2025-02-23T07:57:17.634943Z",
     "shell.execute_reply": "2025-02-23T07:57:17.634354Z",
     "shell.execute_reply.started": "2025-02-23T07:57:17.629632Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleSeg\r\n"
     ]
    }
   ],
   "source": [
    "# 跳转到PaddleSeg目录下，因为下面要使用克隆的源码中的paddleseg包\n",
    "%cd PaddleSeg/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:57:17.636462Z",
     "iopub.status.busy": "2025-02-23T07:57:17.635754Z",
     "iopub.status.idle": "2025-02-23T07:57:17.856962Z",
     "shell.execute_reply": "2025-02-23T07:57:17.855855Z",
     "shell.execute_reply.started": "2025-02-23T07:57:17.636438Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 设置1张可用的卡（如果启动环境是GPU模式的情况下可运行，CPU环境不可运行）\n",
    "! export CUDA_VISIBLE_DEVICES=0  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-02-23T07:57:17.858906Z",
     "iopub.status.busy": "2025-02-23T07:57:17.858446Z",
     "iopub.status.idle": "2025-02-23T07:57:19.807689Z",
     "shell.execute_reply": "2025-02-23T07:57:19.805759Z",
     "shell.execute_reply.started": "2025-02-23T07:57:17.858876Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/aistudio/PaddleSeg/paddleseg/models/layers/ms_deformable_attention.py:110: DeprecationWarning: invalid escape sequence \\s\r\n",
      "  \"\"\"\r\n",
      "/home/aistudio/PaddleSeg/paddleseg/models/losses/rmi_loss.py:78: DeprecationWarning: invalid escape sequence \\i\r\n",
      "  \"\"\"\r\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'skimage'",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
      "\u001B[0;32m/tmp/ipykernel_355/4177293196.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m# 构建数据集\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdatasets\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mDataset\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m      3\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtransforms\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mT\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      4\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      5\u001B[0m \u001B[0;31m# The transforms must be a list!\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PaddleSeg/paddleseg/__init__.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     13\u001B[0m \u001B[0;31m# limitations under the License.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     14\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 15\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mmodels\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdatasets\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtransforms\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moptimizers\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     16\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     17\u001B[0m \u001B[0m__version__\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m'0.0.0.dev0'\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PaddleSeg/paddleseg/models/__init__.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     72\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mlpsnet\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mLPSNet\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     73\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mefficientformerv2_seg\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mEfficientFormerSeg\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 74\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mmaskformer\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mMaskFormer\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     75\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0msegnext\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mSegNeXt\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     76\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mknet\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mKNet\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PaddleSeg/paddleseg/models/maskformer.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     25\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcvlibs\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mmanager\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mparam_init\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     26\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mutils\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mutils\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 27\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcore\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mtrain\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mcheck_logits_losses\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     28\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     29\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PaddleSeg/paddleseg/core/__init__.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     13\u001B[0m \u001B[0;31m# limitations under the License.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     14\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 15\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mtrain\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mtrain\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     16\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mval\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mevaluate\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     17\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0;34m.\u001B[0m\u001B[0mpredict\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mpredict\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PaddleSeg/paddleseg/core/train.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     28\u001B[0m                              \u001B[0mworker_init_fn\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mtrain_profiler\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mop_flops_funs\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     29\u001B[0m                              init_ema_params, update_ema_model, logger)\n\u001B[0;32m---> 30\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcore\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mval\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mevaluate\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     31\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcore\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mexport\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mexport\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0msave_model_info\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mupdate_train_results\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     32\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mutils\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mlogger\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0msetup_logger\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PaddleSeg/paddleseg/core/val.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     21\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     22\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mutils\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mmetrics\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mTimeAverager\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mcalculate_eta\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mlogger\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mprogbar\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 23\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mpaddleseg\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcore\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0minfer\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     24\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     25\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mset_printoptions\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msuppress\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/PaddleSeg/paddleseg/core/infer.py\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     20\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mpaddle\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     21\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mpaddle\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mnn\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfunctional\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mF\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 22\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mskimage\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mmeasure\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmorphology\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m\u001B[1;32m     23\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     24\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'skimage'"
     ]
    }
   ],
   "source": [
    "# 构建数据集\n",
    "from paddleseg.datasets import Dataset\n",
    "import paddleseg.transforms as T\n",
    "\n",
    "# The transforms must be a list!\n",
    "train_transforms = [\n",
    "    T.Resize(target_size=SIZE),\n",
    "    T.RandomHorizontalFlip(),\n",
    "    T.Normalize()\n",
    "]\n",
    "\n",
    "val_transforms = [\n",
    "    T.Resize(target_size=SIZE),\n",
    "    T.Normalize()\n",
    "]\n",
    "\n",
    "train_dataset = Dataset(\n",
    "    mode='train',\n",
    "    dataset_root=DATA_ROOT,\n",
    "    transforms=train_transforms,\n",
    "    num_classes=NUM_CLASSES,\n",
    "    train_path=TRAIN_PATH,\n",
    ")\n",
    "\n",
    "val_dataset = Dataset(\n",
    "    mode='val',\n",
    "    dataset_root=DATA_ROOT,\n",
    "    transforms=val_transforms,\n",
    "    num_classes=NUM_CLASSES,\n",
    "    val_path=VAL_PATH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.808866Z",
     "iopub.status.idle": "2025-02-23T07:57:19.809194Z",
     "shell.execute_reply": "2025-02-23T07:57:19.809069Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.809056Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 构建模型\n",
    "import paddle\n",
    "from paddleseg.models import DeepLabV3\n",
    "from paddleseg.models.backbones import ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet101_vd, ResNet152_vd\n",
    "from paddleseg.models.losses import CrossEntropyLoss\n",
    "\n",
    "# pretrained参数，使用paddle预训练的deeplabv3_resnet50模型\n",
    "model = DeepLabV3(\n",
    "    num_classes=NUM_CLASSES,\n",
    "    backbone=ResNet50_vd(), # currently support Resnet50_vd/Resnet101_vd/Xception65.\n",
    "    pretrained=\"https://bj.bcebos.com/paddleseg/dygraph/cityscapes/deeplabv3_resnet50_os8_cityscapes_1024x512_80k/model.pdparams\"\n",
    ")\n",
    "\n",
    "# 设置学习率、优化器\n",
    "base_lr = 0.01\n",
    "lr = paddle.optimizer.lr.PolynomialDecay(base_lr, power=0.9, decay_steps=3000, end_lr=0)\n",
    "optimizer = paddle.optimizer.SGD(lr, parameters=model.parameters(), weight_decay=4.0e-5)\n",
    "\n",
    "# 设置损失函数\n",
    "losses = {}\n",
    "losses['types'] = [CrossEntropyLoss()] * 1\n",
    "losses['coef'] = [1]* 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.809998Z",
     "iopub.status.idle": "2025-02-23T07:57:19.810229Z",
     "shell.execute_reply": "2025-02-23T07:57:19.810130Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.810119Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 开始训练\n",
    "from paddleseg.core import train\n",
    "\n",
    "train(\n",
    "    model=model,\n",
    "    train_dataset=train_dataset,\n",
    "    val_dataset=val_dataset,\n",
    "    optimizer=optimizer,\n",
    "    losses=losses,\n",
    "    save_dir=EXP_DIR, # 模型和visualdl日志文件的保存根路径\n",
    "    iters=ITERS, # 训练迭代次数\n",
    "    batch_size=BACH_SIZE, # 单卡batch size\n",
    "    save_interval=SAVE_ITERS, # 模型保存的间隔步数\n",
    "    log_iters=LOG_ITERS, # 打印日志的间隔步数\n",
    "    num_workers=NUM_WORKERS, # 用于异步读取数据的进程数量， 大于等于1时开启子进程读取数据\n",
    "    use_vdl=True, # 是否开启visualdl记录训练数据\n",
    ")\n",
    "\n",
    "# 开启visualdl后，可通过可视化功能，监测训练过程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、模型评估与预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1 模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.811341Z",
     "iopub.status.idle": "2025-02-23T07:57:19.811787Z",
     "shell.execute_reply": "2025-02-23T07:57:19.811670Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.811658Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 加载模型\n",
    "import paddle\n",
    "from paddleseg.models import DeepLabV3\n",
    "from paddleseg.models.backbones import ResNet18_vd, ResNet34_vd, ResNet50_vd, ResNet101_vd, ResNet152_vd\n",
    "\n",
    "\n",
    "model = DeepLabV3(\n",
    "    num_classes=NUM_CLASSES,\n",
    "    backbone=ResNet50_vd(), # currently support Resnet50_vd/Resnet101_vd/Xception65.\n",
    "    pretrained=None\n",
    ")\n",
    "\n",
    "# 设置模型参数\n",
    "model_path = '../../output/best_model/model.pdparams'\n",
    "if model_path:\n",
    "    para_state_dict = paddle.load(model_path)\n",
    "    model.set_dict(para_state_dict)\n",
    "    print('Loaded trained params of model successfully')\n",
    "else: \n",
    "    raise ValueError('The model_path is wrong: {}'.format(model_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.812499Z",
     "iopub.status.idle": "2025-02-23T07:57:19.812709Z",
     "shell.execute_reply": "2025-02-23T07:57:19.812608Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.812598Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 对测试集评估（也可评估验证集，这里只评估了测试集）\n",
    "\n",
    "import paddleseg.transforms as T\n",
    "from paddleseg.datasets import Dataset\n",
    "from paddleseg.core import evaluate\n",
    "\n",
    "# 构建测试集\n",
    "# The transforms must be a list!\n",
    "test_transforms = [\n",
    "    T.Resize(target_size=SIZE),\n",
    "    T.Normalize()\n",
    "]\n",
    "\n",
    "test_dataset = Dataset(\n",
    "    mode='val', # 评估模式\n",
    "    dataset_root=DATA_ROOT,\n",
    "    transforms=test_transforms,\n",
    "    num_classes=NUM_CLASSES,\n",
    "    val_path=TEST_PATH, # 评估测试集\n",
    ")\n",
    "\n",
    "# 评估测试集\n",
    "evaluate(model, test_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "由上可知测试集评估结果如下：\n",
    "```\n",
    "\n",
    "###### 总体数据\n",
    "\n",
    "| Images | mIoU | Acc | Kappa | Dice |\n",
    "| -------- | -------- | -------- |-------- | -------- |\n",
    "| 665     | 0.5931     | 0.8185     | 0.7626     | 0.7305     |\n",
    "\n",
    "\n",
    "###### 每一类数据\n",
    "\n",
    "| Class| 未知 | 裸地| 草地| 构筑物 | 道路| 林地| 水域| 耕地 | 房屋|\n",
    "| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |\n",
    "| Class label | 0     | 1     | 2     | 3     | 4     | 5     | 6     | 7     | 8     |\n",
    "| Class IoU     | 0.5812| 0.373| 0.5463| 0.2988|  0.6272|  0.8699|  0.7247|  0.6914|  0.6256| \n",
    "| Class Precision|  0.8196 | 0.4734 | 0.6953 | 0.5994 | 0.791  | 0.9095 | 0.8701 | 0.8347 | 0.7112| \n",
    "| Class Recall|  0.6665 | 0.6377|  0.7183 | 0.3734|  0.7517|  0.9523|  0.8127|  0.801 |  0.8387| \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2 模型预测（用predict API方式）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.813479Z",
     "iopub.status.idle": "2025-02-23T07:57:19.813681Z",
     "shell.execute_reply": "2025-02-23T07:57:19.813585Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.813576Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "\n",
    "def get_image_list(data_root, test_path):\n",
    "    \"\"\"\n",
    "    Get image list from the test path\n",
    "    \"\"\"\n",
    "\n",
    "    image_list = []\n",
    "    with open(test_path, 'r') as file:\n",
    "        lines = file.readlines()\n",
    "        for line in lines:\n",
    "            image_path = os.path.join(data_root , line.split(' ')[0])\n",
    "            if os.path.isfile(image_path):\n",
    "                image_list.append(image_path)\n",
    "\n",
    "    if len(image_list) == 0:\n",
    "        raise RuntimeError('There are not image file in `--image_path`')\n",
    "\n",
    "    return image_list\n",
    "\n",
    "# 测试集图像文件目录、文件名列表\n",
    "image_dir = os.path.join(DATA_ROOT, 'images')\n",
    "image_list = get_image_list(DATA_ROOT, TEST_PATH)\n",
    "# print(image_list)\n",
    "# print(image_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.814549Z",
     "iopub.status.idle": "2025-02-23T07:57:19.814774Z",
     "shell.execute_reply": "2025-02-23T07:57:19.814677Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.814668Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 预测测试集\n",
    "import paddleseg.transforms as T\n",
    "from paddleseg.core import predict\n",
    "\n",
    "\n",
    "# 用predict API测试时，一定要是T.Compose类型\n",
    "test_transforms = T.Compose([\n",
    "    T.Resize(target_size=SIZE),\n",
    "    T.Normalize()\n",
    "])\n",
    "\n",
    "predict(\n",
    "    model,\n",
    "    model_path=model_path,  # 模型路径\n",
    "    transforms=test_transforms, # transform.Compose, 对输入图像进行预处理\n",
    "    image_list=image_list, # list, 待预测的图像路径列表。\n",
    "    image_dir=image_dir, # str, 待预测的图片所在目录\n",
    "    save_dir=TEST_SAVE_DIR #str, 结果输出路径\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "预测结果保存位置\n",
    "/home/aistudio/PaddleSeg/output/results/added_prediction\n",
    "/home/aistudio/PaddleSeg/output/results/pseudo_color_prediction\n",
    "```\n",
    "\n",
    "| 原始图片 | 叠加预测结果 | 预测结果 |\n",
    "| -------- | -------- | -------- |\n",
    "| ![](https://ai-studio-static-online.cdn.bcebos.com/0d3fe9208eaa4845ad99dcdb8b49215f11f3e31c3bfb45e8b50c0fc55d71fbd1)     | ![](https://ai-studio-static-online.cdn.bcebos.com/4860dfab05d04f0cb1b033059d9f0ed760452877a90147efa48031827981f557)    | ![](https://ai-studio-static-online.cdn.bcebos.com/21554e7a223a43e3894b83efef2487c58cc974a979f144c2a0e8d1714916005a)     |\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.3 模型预测（非API方式，随机预测测试集并可视化结果）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.815765Z",
     "iopub.status.idle": "2025-02-23T07:57:19.816028Z",
     "shell.execute_reply": "2025-02-23T07:57:19.815907Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.815896Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 可视化配置\n",
    "import numpy as np\n",
    "\n",
    "# 类别标签的颜色配置\n",
    "color_map = {\n",
    "    0: [0, 0, 0],        # '未知': 类别 0\n",
    "    1: [215, 200, 185],  # '裸地': 类别 1\n",
    "    2: [131, 194, 56],   # '草地': 类别 2\n",
    "    3: [241, 165, 180],  # '构筑物':  类别 3\n",
    "    4: [210, 216, 201],  # '道路': 类别 4\n",
    "    5: [49, 173, 105],   # '林地': 类别 5\n",
    "    6: [163, 214, 245],  # '水域': 类别 6\n",
    "    7: [248, 208, 114],  # '耕地': 类别 7\n",
    "    8: [229, 103, 102]   # '房屋': 类别 8\n",
    "}\n",
    "\n",
    "def label2color(lable):\n",
    "    '''\n",
    "    将单通道灰度label, 根据颜色配置转为三通道彩色\n",
    "    '''\n",
    "    # 将图像转换为 numpy 数组\n",
    "    label_array = np.array(lable)\n",
    "    # 创建彩色图像\n",
    "    color_label_array = np.zeros((label_array.shape[0], label_array.shape[1], 3), dtype=np.uint8)\n",
    "\n",
    "    # 将单通道标签映射为彩色图像\n",
    "    for label_value, color in color_map.items():\n",
    "        color_label_array[label_array == label_value] = color\n",
    "    return color_label_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.817229Z",
     "iopub.status.idle": "2025-02-23T07:57:19.817785Z",
     "shell.execute_reply": "2025-02-23T07:57:19.817662Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.817648Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 构建测试集\n",
    "import paddleseg.transforms as T\n",
    "from paddleseg.datasets import Dataset\n",
    "\n",
    "# 这个数据增强一定要是list类型！！！ 上面用predict API测试时是T.Compose类型\n",
    "test_transforms = [\n",
    "    T.Resize(target_size=SIZE),\n",
    "    T.Normalize()\n",
    "]\n",
    "\n",
    "test_dataset = Dataset(\n",
    "    mode='test',\n",
    "    dataset_root=DATA_ROOT,\n",
    "    transforms=test_transforms,\n",
    "    num_classes=NUM_CLASSES,\n",
    "    test_path=TEST_PATH,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2025-02-23T07:57:19.818412Z",
     "iopub.status.idle": "2025-02-23T07:57:19.818638Z",
     "shell.execute_reply": "2025-02-23T07:57:19.818527Z",
     "shell.execute_reply.started": "2025-02-23T07:57:19.818517Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 可视化\n",
    "import random\n",
    "import paddle\n",
    "from paddleseg.models import BiSeNetV2\n",
    "from paddleseg.models import UNet\n",
    "import paddleseg.transforms as T\n",
    "from paddleseg.core import infer\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from paddleseg.core import infer\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# 需要展示的样本个数\n",
    "num_imgs_to_show = 4\n",
    "# 随机抽取样本\n",
    "chosen_indices = random.sample(range(len(test_dataset)), k=num_imgs_to_show)\n",
    "# print(chosen_indices)\n",
    "\n",
    "# 读取输入影像\n",
    "image_paths = [test_dataset.file_list[idx][0] for idx in chosen_indices]\n",
    "# 读取真值标签\n",
    "label_paths = [test_dataset.file_list[idx][1] for idx in chosen_indices]\n",
    "\n",
    "# 获取模型预测输出\n",
    "preds = []\n",
    "with paddle.no_grad():\n",
    "    model.eval()\n",
    "    for idx in chosen_indices:\n",
    "        image = test_dataset[idx]['img']\n",
    "        # C,H,W -> 1,C,H,W\n",
    "        image = paddle.to_tensor(image).unsqueeze(0) \n",
    "        # 预测推理\n",
    "        pred = infer.inference(model, image)  \n",
    "        # 对pred进行argmax操作, 并转换为NumPy数组\n",
    "        pred = paddle.argmax(pred, axis=1).numpy()\n",
    "        # 调整数组形状为SIZE, 并将数据类型转换为np.uint8\n",
    "        pred = np.reshape(pred, SIZE).astype(np.uint8)\n",
    "        # 将预测结果转为彩色\n",
    "        preds.append(label2color(pred))\n",
    "\n",
    "# 绘制图片\n",
    "fig, axes = plt.subplots(num_imgs_to_show, 3, figsize=(1 * 8, 1 * 8))\n",
    "fig.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0.01, wspace=0.01)\n",
    "\n",
    "# 给每列添加标题\n",
    "axes[0, 0].set_title('Input Image')\n",
    "axes[0, 1].set_title('Ground Truth Label')\n",
    "axes[0, 2].set_title('Predicted Label')\n",
    "\n",
    "for i in range(num_imgs_to_show):\n",
    "    axes[i, 0].imshow(Image.open(image_paths[i]))\n",
    "    axes[i, 0].axis('off')\n",
    "    axes[i, 1].imshow(label2color(Image.open(label_paths[i])))\n",
    "    axes[i, 1].axis('off')\n",
    "    axes[i, 2].imshow(preds[i])\n",
    "    axes[i, 2].axis('off')\n",
    "plt.show()"
   ]
  }
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